code: def comm_get_feature_extractor_inputs(
      self,
      with_segmentation_maps=False,
      is_instance_map=False,
      segmentation_type="np",
  ):
      feature_extractor = self.feature_extraction_class(
          **self.feat_extract_dict
      )
      # prepare image and target
      batch_size = self.feature_extract_tester.batch_size
      num_labels = self.feature_extract_tester.num_labels
      annotations = None
      instance_id_to_semantic_id = None
      if with_segmentation_maps:
          high = num_labels
          if is_instance_map:
              high * 2
              labels_expanded = list(range(num_labels)) * 2
              instance_id_to_semantic_id = {
                  instance_id: label_id
                  for instance_id, label_id in enumerate(
                      labels_expanded
                  )
              }
          annotations = [
              np.random.randint(0, high, (384, 384)).astype(np.uint8)
              for _ in range(batch_size)
          ]
          if segmentation_type == "pil":
              annotations = [
                  Image.fromarray(annotation)
                  for annotation in annotations
              ]
  
      image_inputs = prepare_image_inputs(
          self.feature_extract_tester, equal_resolution=False
      )
      inputs = feature_extractor(
          image_inputs,
          annotations,
          return_tensors="pt",
          instance_id_to_semantic_id=instance_id_to_semantic_id,
          pad_and_return_pixel_mask=True,
      )
  
      return inputs
  
imports: import unittest
  import numpy
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
label_desc: Use image node_id 9 for calling the MaskFormerFeatureExtractionTest obj's underlying member method code with example usage: obj.comm_get_feature_extractor_inputs(with_segmentation_maps, is_instance_map, segmentation_type) and returns: inputs